Biology subjects

Jiang, Z.

Publications and source records attributed to Jiang, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Discovery of Abundant Nano-scale Lymphatic-like Vessels in Brains

As one of the most metabolically active organs, the brain requires an exceptionally efficient system for waste clearance to sustain its high metabolic demands. However, whether such a system exists--and, if so, what structural features enable its efficiency--remains incompletely understood. More than a decade ago, the "glymphatic system" was proposed to describe neurofluid transport through cerebrospinal fluid (CSF) and perivascular spaces (PVS), in conjunction with dural and meningeal lymphatic pathways. Nevertheless, it remains unresolved whether neurofluid transport is organized as a structured, vessel-like flow network. Moreover, in stark contrast to the dense network of blood capillaries in the brain, only a sparse population of lymphatic vessels has been identified, raising doubts as to whether the currently recognized lymphatic architecture alone can support efficient metabolic waste clearance. By combining expansion microscopy with CRANAD-3, a pan-{beta}-amyloid fluorescent probe, we discovered abundant nanoscale lymphatic-like vessels (NLVs) within the brain parenchyma of both mice and humans. The majority of these structures have diameters below 1,000 nm and exhibit moderate positivity for multiple lymphatic markers, including LYVE-1, Prox-1, PDPN, and VEGFR3. NLVs frequently coil around blood vessels, and putative connections between vascular structures were observed. Notably, some NLVs traverse multiple cortical layers and display distinct orientation patterns that vary across cortical laminae. This discovery reveals a previously "hidden" vascular network in the brain parenchyma and raises the possibility that an abundant, highly organized system of nanoscale tubular structures may provide an efficient conduit for metabolic waste clearance. Such a system could represent a critical, previously unrecognized component supporting the brains extraordinary metabolic demands.

neuroscience↗

bioRxiv2025

The natural flavonoid dihydromyricetin targets senescent cells via PRDX2 and alleviates age-related diseases

Aging is a preeminent risk factor for chronic diseases, with cellular senescence as one of the major hallmarks and an effective target to delay, prevent or alleviate age-related disorders. Here we report in vitro screening outputs from a natural medicinal agent (NMA) library, wherein dihydromyricetin (DMY), a natural flavonoid, showed senotherapeutic potential. DMY protected senescent fibroblasts from further DNA damage and attenuated the senescence-associated secretory phenotype (SASP), acting as a senomorphic agent. Proteomics suggested that DMY promotes nuclear translocation of peroxiredoxin 2 (PRDX2), a reactive oxygen species (ROS) scavenger, facilitating DNA repair in senescent cells. In prematurely aged mice, DMY administration mitigated tissue aging changes and age-related physiological decline. In anticancer regimens, DMY improved outcomes of chemotherapy. However, DMY demonstrated senolytic activity against senescent microglial cells, wherein basal PRDX2 expression remains low, by impairing mitochondrial function to promote ROS-mediated apoptosis. In mice developing an Alzheimers disease (AD)-like state, DMY eliminated senescent microglial cells from amyloid {beta}-protein (A{beta}) plaques, alleviating A{beta}-associated pathological phenotypes. Together, our study proposes DMY to be a natural senotherapeutic agent representing a therapeutic solution for mitigating age-related morbidities including but not limited to cancers and AD.

cell biology↗

bioRxiv2025

The Impact of Variant Calling on Substitution Mutational Signature Inference

Identifying mutational signatures is a key component of cancer genomics studies, yet the influence of variant calling strategies on signature extraction has not been systematically evaluated. Here, we analyzed over 8,900 whole exomes from The Cancer Genome Atlas (TCGA) and over 1,800 whole genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium to assess how mutation callers shape de novo single-base substitution (SBS) signatures. We found that consensus calling yielded stable de novo signatures across reference genomes and pipeline versions, whereas individual callers introduced false-positive SBSs that manifested as artifactual signatures that were reproducibly detected by three independent signatures extraction tools. A minimal consensus approach requiring agreement between only two variant calling algorithms effectively removed these artifacts while preserving true biological signal. Together, these results establish consensus variant calling as essential for robust inference of de novo SBS mutational signatures and provide practical guidelines for distinguishing genuine mutational processes from technical artifacts.

bioinformatics↗

bioRxiv2025

Hepatocyte FGFR2 Mediates the Antifibrotic Effects of FGF10 in Advanced Liver Fibrosis.

Fibroblast growth factor 10 (FGF10) supports epithelial repair, but its role in liver fibrogenesis remains uncertain. Here we found that hepatic FGF10 and its receptor FGFR2 decline with progression to advanced fibrosis in patient biopsies and in carbon tetrachloride (CCl4)- and diet-induced mouse models. Restoring hepatic FGF10 with recombinant human FGF10 or adeno-associated virus mediated liver-targeted expression attenuated and partially reversed established advanced bridging fibrosis, reduced inflammation, and decreased hepatocyte apoptosis, and these benefits required hepatocyte FGFR2 because hepatocyte-specific Fgfr2 deletion abolished protection. In primary hepatocytes, FGF10 activated FGFR2-FRS2, increased inhibitory GSK3{beta}(Ser9) phosphorylation, and suppressed NF-{kappa}B activation, reducing TGF-{beta}1 and other cytokines and thereby limiting paracrine hepatic stellate cells activation. The molecular and histologic benefits extended to metabolically primed steatohepatitis, supporting translatability beyond toxicant injury. Mechanistic signaling readouts confirm FGFR2 engagement, while the main text centers on the hepatocyte FGF10-FGFR2 axis as an intrinsic brake on fibrogenesis. Together, these data identify a hepatocyte-centric FGF10-FGFR2 axis as a negative regulator and potential regressor of fibrogenesis, highlighting it as a tractable therapeutic target in advanced liver fibrosis.

pharmacology and toxicology↗

bioRxiv2025

Automated Seizure Detection in Animal EEG Signals

Automated seizure detection in animal electroencephalography (EEG) is crucial for accelerating epilepsy research. While machine learning (ML) and deep learning (DL) techniques have shown promise in seizure detection for human EEG and some rodent models, their application to chemically diverse animal data remains limited. In particular, no prior work has explored deep learning approaches for EEG data where seizure/epilepsy was induced by exposure to Soman (GD), a potent agent known to generate complex and variable seizure dynamics. In this work, we evaluate deep recurrent neural networks--Gated Recurrent Units (GRU) and Long Short-Term Memory networks (LSTM)--for seizure detection in EEG signals acquired by single channel (bipotential electrodes) from the rats exposed to either kainate or GD, which later animals developed epilepsy (measured by spontaneously recurring seizures). We benchmark these models against classical baselines including Random Forest and XGBoost, using intracranial EEG data from eight animals. Our results show that GRU and LSTM substantially outperform other shallow models in both accuracy and robustness across EEG traces.

animal behavior and cognition↗

bioRxiv2025

BMP-Smad1/9 signaling is required for PGC proliferation in zebrafish

The specification and maintenance of germ cell lineage are vital for reproductive development and heredity. The germ cell fate in some species, such as zebrafish and Xenopus, is determined by maternally supplied germ plasm, whereas in mammals it is induced by bone morphogenetic protein (BMP) signaling after implantation. It remains elusive whether BMP signaling is implicated in zebrafish germ cell development. Here, we demonstrate that, in zebrafish, BMP-Smad1/9 signaling is required for primordial germ cell (PGC) maintenance but not for PGC fate determination or migration. BMP inhibitor treatment or whole-embryo smad1/9 knockdown reduces the number of PGCs. To investigate cell-autonomous roles of BMP-Smad1/9 in PGCs, we generated PGC-specific smad1/9 knockouts using a double-transgenic approach with PGC-specific expression of Cas9 and ubiquitous expression of guide RNAs (gRNAs). Loss of Smad1/9 in PGCs leads to impaired PGC proliferation and increased apoptosis, resulting in reduced PGC numbers and adult sex bias. Moreover, transcriptome analysis of smad1-deficient PGCs revealed no significant changes in the expression of PGC-specific genes, but a marked upregulation of genes involved in cell cycle checkpoints and DNA damage repair. Notably, ectopic ATR-pChk1 activation in smad1-cKO PGCs validates cell cycle defects and DNA replication stress. ATR inhibition restores PGC numbers in mutant embryos. Collectively, these findings underscore a critical role of BMP-Smad1/9 signaling in zebrafish PGC population maintenance, while it is dispensable for PGC fate determination and migration.

developmental biology↗

bioRxiv2025

CTDP: Identifying cell types associated with disease phenotypes using scRNA-seq data

Single-cell RNA sequencing enables transcriptome-wide analysis at single-cell resolution, offering unprecedented insights into cellular heterogeneity across biological conditions. However, accurately comparing transcriptomic distributions of cells from distinct biological states, such as healthy versus diseased individuals, remains challenging. To address this, we developed CTDP, a robust and interpretable computational framework that identifies disease phenotype-associated cell types of interest by integrating Lasso-regularized logistic regression with permutation testing. Through comprehensive evaluations on both simulated and real-world datasets, including melanoma immunotherapy, COVID-19 severity, and liver cirrhosis, CTDP consistently outperformed existing methods such as DA-seq, scDist, and PENCIL in both accuracy and robustness. In melanoma, CTDP uncovered immune-responsive clusters and revealed transcriptional regulators like PTPRC, CREM, and JUNB linked to immunotherapy efficacy. In COVID-19, it identified critical severity-associated cell types, such as B cells, NK cells, epithelial cells, and macrophages, which contribute to dysregulated immune responses and inflammation in severe cases. These results highlight CTDPs power in uncovering disease-relevant cell populations and its potential to advance precision medicine through single-cell analysis.

bioinformatics↗

bioRxiv2025

Visuospatial attention exerts opposite modulatory effects on explicit and implicit audiovisual subjective synchrony via the frontoparietal network

Synchronization and integration constitute two essential subprocesses of audiovisual binding, commonly indexed by explicit subjective synchrony (ESS) and implicit subjective synchrony (ISS), respectively. Previous studies have shown that spontaneous fluctuations in visual attention are associated with ESS, and that ESS and ISS are negatively correlated, consistent with the temporal renormalization theory. However, it remains unknown whether controlled manipulations of visuospatial attention can causally modulate ESS and ISS in opposite directions via distinct neural mechanisms. To address this question, we conducted two complementary EEG experiments. In Experiment 1, ESS was assessed using a spatially cued beep-flash synchrony judgment task and the point of subjective simultaneity (PSS). In Experiment 2, ISS was measured using fusion and fission variants of the sound- induced flash illusion, with the peak illusion rate (PIR) as the behavioral index. This dual-paradigm design enabled direct comparison between explicit and implicit synchrony under matched attentional manipulations. Behaviorally, asymmetric allocation of visuospatial attention between auditory- and visual-leading conditions induced opposite shifts in PSS and PIR, specifically in the fusion variant. At the neural level, prestimulus frontal theta oscillations were associated with variations in PSS, whereas bilateral frontotemporal and left posterior beta oscillations were associated with variations in PIR. These results demonstrate that visuospatial attention exerts opposite modulatory effects on ESS and ISS through distinct oscillatory dynamics, providing causal evidence for the temporal renormalization theory and advancing understanding of how attentional control shapes multisensory temporal binding.

animal behavior and cognition↗

bioRxiv2025

GatorAffinity: Boosting Protein-Ligand Binding Affinity Prediction with Large-Scale Synthetic Structural Data

Protein-ligand binding affinity prediction is a fundamental task in computational drug discovery. Although substantial efforts have been made to enhance prediction accuracy using data-driven approaches, progress remains limited by persistent data scarcity. The widely used PDBbind dataset, for example, contains fewer than 20, 000 experimental structures with annotated binding affinities, while a vast number of affinity measurements remain underutilized due to missing structural data. Here, we investigate this untapped potential by curating more than 450, 000 synthetic protein-ligand complexes annotated with Kd and Ki values using the Boltz-1 structure prediction model. Building on this unprecedented scale of synthetic data, further augmented with over 1 million synthetic complexes from the recently released SAIR database annotated with IC50 values, we develop GatorAffinity, a geometric deep learning-based scoring function pretrained on large-scale synthetic data and fine-tuned using high-quality experimental structures from PDBbind. Extensive evaluation on a leak-proof benchmark demonstrates that GatorAffinity significantly outperforms state-of-the-art affinity prediction methods, offering superior accuracy and generalizability. Our findings show that augmenting available experimental data with synthetic complexes can effectively address the data scarcity challenge while maintaining strong predictive reliability. By releasing the pretrained GatorAffinity model and the large-scale synthetic dataset GatorAffinity-DB, we provide a scalable and reproducible foundation for affinity prediction, virtual screening, and broader structure-based drug design applications (https://github.com/AIDD-LiLab/GatorAffinity).

bioinformatics↗

bioRxiv2025

From Correlation to Causation: Cell-Type-Specific Gene Regulatory Networks in Alzheimer's Disease

INTRODUCTIONAlzheimers disease (AD) involves complex regulatory disruptions across multiple brain cell types, yet a comprehensive understanding of the intracellular causal mechanisms remains unclear. METHODSWe presented an integrative analysis framework using single-nucleus transcriptomic with matched subject-level genotype data from 272 human AD in the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) study, and constructed causality-based, cell-type-specific gene regulatory networks (GRNs). RESULTSOur method identifies regulatory genes from both transcription factors (TFs) and non-TFs, thereby capturing a complete and accurate causal regulatory map across different brain cell types. This work revealed both established and novel regulations, pathways, and cell-type-unique hub genes in AD. Beyond constructing transcriptome-wide GRNs, we quantitatively assessed hub genes and distinguished those with regulatory or responsive roles. DISCUSSIONOur study provides a comprehensive mapping of cell-type-specific causal GRNs in AD, providing a powerful resource for dynamic pathway exploration, hypothesis generation, and functional interpretation.

neuroscience↗

bioRxiv2025

Tracing the evolutionary origins of human voltage-gated K+ channels: where they come from and what we lost along the way

Humans have 40 voltage-gated K+ channel (Kv) genes that are broadly conserved in vertebrate model organisms and spread across three ancient gene families (KCNQ, EAG and Shaker). We used deep coverage phylogenetic analyses to trace the evolutionary origins of each human Kv channel. 39/40 human channels were already present in the common ancestor of jawed vertebrates (gnathostomes) and as many as 36 could have an origin in a genome duplication within the stem gnathostome lineage. The Elk channel Kv12.2 is the newest human Kv stemming from a gene duplication in the ancestor of tetrapods and lobe-finned fish. Kv5.1, is the oldest human Kv channel, traceable to the stem chordate lineage. We found evidence for a total of 45 widespread gnathostome Kv channels. Humans lost five channels that can still be found in some other gnathostome lineages. Birds and mammals have the smallest remaining Kv channel sets among gnathostomes and a lineage-specific genome duplication in teleost fish increased their ancestral Kv set to as many as 69 channels. We expressed orthologs of the 5 gnathostome channels missing in humans from vampire bat (Kv3.5), harpy eagle (Kv7.1L), duck-billed platypus (Kv8.3), coelacanth (Kv9.4) and axolotl (Kv12.4). Each has some unique functional characteristics, but their gating properties are largely redundant with existing human Kv paralogs. The human Kv set still contains substantial gating redundancy and we argue that loss-of-function mutations driving partitioning and subfunctionalization have probably had a greater influence on the broad retention of ancestral gnathostome Kv channels than gain-of-function gating optimizations. SUMMARY STATEMENTWe used deep coverage phylogenies to trace the origins of 36/40 human voltage-gated K+ channel genes to a common ancestor of jawed vertebrates. We identified and functionally characterized 5 additional channels that humans lost from other living vertebrates.

evolutionary biology↗

bioRxiv2025

A Universal Duplex Sequencing Approach for Accurate Detection of Somatic Mutations

Ultra-accurate detection of rare somatic mutations is critical for understanding mutational processes in human disease, aging, and environmental exposures, yet current methods are limited by error rates, restricted genome coverage, and high DNA input. We present UDSeq, a duplex sequencing protocol combining random fragmentation, efficient UMI ligation, and quantitative input control to achieve near-complete genome/exome representation from as little as 100 pg DNA. Benchmarking in human sperm estimates a UDSeq error rate of [~]2.5x10-9 per base pair. UDSeq captures mutational signatures from heterogeneous populations without clonal expansion, reproduces exposure-specific patterns in cell lines and rodent models, and enables cross-species profiling. Compared with prior duplex methods, UDSeq yields up to fourfold more usable duplex molecules, improves library conversion, and remains cost-effective. We include a step-by-step protocol with quality-control checkpoints for fragment size, ligation yield, library conversion, and duplication rate. UDSeq provides a scalable, low-input platform for accurate profiling of somatic mutagenesis.

genomics↗

bioRxiv2025

Localized Space Coding and Phase Coding Complement Each Other to Achieve Robust and Efficient Spatial Representation

Localized space coding and phase coding are two distinct strategies responsible, respectively, for representing abstract structure and sensory observations in neural cognitive maps. In spatial representation, localized space coding is implemented by place cells in the hippocampus (HPC), while phase coding is implemented by grid cells in the medial entorhinal cortex (MEC). Both strategies have their own advantages and disadvantages, and neither of them meets the requirement of representing space robustly and efficiently in the brain. Here, we show that through reciprocal connections between HPC and MEC, place and grid cells can complement each other to overcome their respective shortcomings. Specifically, we build a coupled network model, in which a continuous attractor neural network (CANN) with position coordinate models place cells, while multiple CANNs with phase coordinates model grid cell modules with varying spacings. The reciprocal connections between place and grid cells encode the correlation prior between the sensory cues processed by HPC and MEC, respectively. Using this model, we show that: 1) place and grid cells interact to integrate sensory cues in a Bayesian manner; 2) place cells complement grid cells in coding accuracy by eliminating non-local errors of the latter; 3) grid cells complement place cells in coding efficiency by enlarging the number of environmental maps stored stably by the latter. We demonstrate that the coupled network model explains the seemingly contradictory experimental findings about the remapping phenomena of place cells when grid cells are either inactivated or depolarized. This study gives us insight into understanding how the brain employs collaborative localized and phase coding to realize both robust and efficient information representation.

neuroscience↗

bioRxiv2025

A Spatially Resolved Atlas of Alternative Polyadenylation Across 18 Human Tissues and 76 Disease States

Alternative polyadenylation (APA) is a key regulator of gene expression and cellular dynamics, yet systematic investigations of spatially resolved APA across diverse human tissues remain limited. Here, we developed SpatialAPA (https://github.com/Omicslab-Zhang/spatialAPA), a framework that benchmarks multiple APA identification methods and integrates spatial APA data with gene expression and cellular dynamics at spatial resolution. Applying SpatialAPA to 363 spatial transcriptomic data from 56 projects across 18 human tissues and 76 diseases, we constructed a spatially resolved APA atlas comprising 346,932 APA events across 52,175 genes. This atlas reveals organ-specific APA patterns and provides new insights into how APA regulates tissue homeostasis and disease progression beyond transcriptional control. To ensure cross-sample comparability, we applied batch correction, while spatial cell deconvolution uncovered cell-type-specific dynamics and interactions. In triple-negative breast cancer, integrated spatial and single-cell analyses identified TSPAN8-positive epithelial subpopulations whose distinct APA regulation and transcriptional programs drive differentiation and malignant progression. To facilitate community access, we developed an online platform (http://www.biomedical-web.com/spatialAPAdb/home) for exploring APA, gene expression, and cellular dynamics in health and disease. Together, this study establishes the first comprehensive spatial APA atlas, providing a valuable resource and analytical framework for investigating molecular mechanisms and therapeutic targets.

bioinformatics↗

bioRxiv2025

An integrated multi-tissue atlas of epigenomic landscapes and regulatory elements in the bovine genome

Deciphering the regulatory syntax of the genome is essential to understand the genetic and molecular architecture of complex traits, as most trait-associated variants lie in non-coding regions. Yet, functional annotation of the bovine genome remains limited, hindering our ability to unravel the mechanisms underpinning complex traits of economic and ecological importance in cattle. Here, we present a comprehensive epigenetic atlas comprising 1,138 genome-wide epigenetic profiles, including chromatin accessibility, six histone modifications, CCCTC-binding factor (CTCF) transcription factor binding, DNA methylation, chromatin conformation, and transcriptomes across 53 adult tissues, five fetal tissues, and seven primary cell types. This atlas-level data enables us to annotate around 45% of the genome as putative regulatory elements exhibiting tissue- or cell-specific regulatory activity. Leveraging sequence-to-function deep learning models, we discovered 301 sequence motifs and predicted the functional impact of genetic variants through in silico mutagenesis, thereby facilitating the decoding of the regulatory syntax of the cattle genome and fine-mapping of GWAS loci for 22 complex traits. Cross-species analysis further revealed evolutionarily conserved features of regulatory architecture and provided evolutionary insights into complex traits and diseases in humans. Together, this atlas offers a foundational resource for advancing cattle functional genomics, sustainable breeding, and studies of regulatory evolution.

genomics↗

bioRxiv2025

CART-GPT: A T Cell-Informed AI Linguistic Framework for Interpreting Neurotoxicity and Therapeutic Outcomes in CAR-T Therapy

Chimeric antigen receptor (CAR) T cell therapy holds transformative potential for hematologic malignancies, yet predicting patient-specific treatment efficacy and neurotoxicity remains a major clinical challenge due to the complex and heterogeneous nature of the infused CAR-T cell populations. Here, we introduce CART-GPT, a transformer-based model fine-tuned on a curated atlas of 1.12 million CAR-T single-cell RNA-seq profiles annotated with clinical outcomes. CART-GPT is the first AI model developed for CAR-T therapy that predicts both treatment response and the risk of immune effector cell-associated neurotoxicity syndrome (ICANS), achieving state-of-the-art performance (AUC ~0.8) and marking a significant advance in the field. The model provides interpretable insights, revealing that neither therapeutic efficacy nor neurotoxicity is driven by individual cell types alone, but by the combined influence of discrete, distinct subsets across diverse T cell states and transcriptional programs. A novel cell aggregation strategy links single-cell predictions to patient-level metrics, enhancing both accuracy and biological relevance. As a contribution to this ever-evolving field, we also release a comprehensive, annotated single-cell CAR-T atlas as a community resource to facilitate future research in immunotherapy. These advances demonstrate the potential of foundation models in single-cell biology to inform precision CAR-T treatment planning and facilitate the rational design of next-generation cell therapies.

bioinformatics↗

bioRxiv2025

Neural Correlates of Trial Outcome Monitoring during Long-term Learning in Primate Posterior Parietal Cortex

Monitoring behavioral outcomes is crucial for optimizing behavior and learning new tasks. However, how the brain monitors outcome information during long-term learning remains largely unknown. Using two-photon calcium imaging in behaving monkeys, we tracked neuronal activity in area 7a of the posterior parietal cortex (PPC), an important associative cortex that has not been typically implicated in associative learning (AL), while the macaque monkeys learned new visual-motor associations over multiple days. We found robust neuronal representation of salient behavioral outcomes (correct vs. incorrect) which closely correlated with the monkeys learning behavior. Furthermore, outcome representation significantly reorganized when monkeys transitioned to learn novel associations, followed by gradual evolution over subsequent learning days, a process constrained by the functional connectivity among neurons within local network. These suggest a substantive role for primate PPC in long-term AL through monitoring trial outcome, and indicate a principle for long-term learning: network connectivity constrains the evolution of neural encodings.

neuroscience↗

bioRxiv2025

Netrin-1 promotes pancreatic tumorigenesis and innervation through NEO1.

Nerves have been shown to regulate cancer progression. However, a clear demonstration of a role for axon guidance molecules in pancreatic tumorigenesis, innervation, and metastasis has been lacking. Using murine KrasG12D-mutant pancreatic organoids, we screened axon guidance molecules by qRT-PCR, identified Ntn1 upregulation, and then verified its in vivo upregulation during pancreatic tumorigenesis in humans and mice. NTN1 and its receptor NEO1 were upregulated in epithelial cells by the Kras mutation and {beta}-adrenergic signaling, in part, through the MAPK pathway. Ex-vivo culture of celiac ganglia showed that NTN1 promoted the axonogenesis of sympathetic neurons through the nerve NEO1 receptor. In the Pdx1-Cre;LSL-KrasG12D/+ model, Ntn1 knockout decreased sympathetic innervation and the development of pancreatic intraepithelial neoplasia. Treatment of pancreatic tumor organoids with recombinant NTN1 enhanced cell growth, epithelial-mesenchymal transition (EMT), and cancer stemness with the upregulation of ZEB1 and SOX9 through NEO1-mediated activation of focal adhesion kinase (FAK). In Pdx1-Cre;LSL-KrasG12D/+;LSL-Trp53R172H/+mice, Ntn1 knockout reduced innervation, FAK phosphorylation, and the features of EMT and stemness to extend mouse survival. In a liver metastasis model of PDAC (pancreatic ductal adenocarcinoma), treatment with a NTN1-neutralizing antibody or tumoral knockdown of Neo1 reduced ZEB1 and SOX9 and decreased tumor progression. In contrast, Ntn1 overexpression promoted innervation and the progression of PDAC liver metastasis. These data suggest that the NTN1/NEO1 axis is a key regulator of PDAC progression, directly influencing cancer cell stemness and EMT, while indirectly promoting tumor growth through nerves. Inhibiting the NTN1/NEO1 axis could represent a potential therapeutic approach for PDAC. Statement of SignificanceNTN1 promotes pancreatic tumorigenesis and metastasis directly and indirectly through nerves, highlighting the importance of tumor cell-nerve crosstalk in cancer. NTN1 blockade could represent a promising strategy for treating PDAC liver metastasis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/666009v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@ca611corg.highwire.dtl.DTLVardef@ad9264org.highwire.dtl.DTLVardef@1661c00org.highwire.dtl.DTLVardef@b86798_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗